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Load packages

library(gsheet)
library(tidyverse)
library(quanteda)
library(ggplot2)
library(plotly)

Read data and inspect

url <- "https://docs.google.com/spreadsheets/d/1qmbPdvspf9Vg_Eab9fT34ubudF_vklJwKxf0UuJrHvw/edit?usp=sharing"

rawDF <- gsheet2tbl(url)

nameVec <- c("time", "github", "languages", "proficiency")
colnames(rawDF) <- nameVec
summary(rawDF)
     time              github           languages          proficiency  
 Length:2           Length:2           Length:2           Min.   :3.00  
 Class :character   Class :character   Class :character   1st Qu.:3.25  
 Mode  :character   Mode  :character   Mode  :character   Median :3.50  
                                                          Mean   :3.50  
                                                          3rd Qu.:3.75  
                                                          Max.   :4.00  
head(rawDF)

Count languages

languages <- as.character(rawDF$languages) %>% corpus
summary(languages)
Corpus consisting of 2 documents, showing 2 documents:

  Text Types Tokens Sentences
 text1     6      9         1
 text2     3      3         1
dfm <- tokens(languages) %>%
  dfm  %>%
  convert(to="data.frame") %>%
  select(!contains(",")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm = TRUE))) %>%
  pivot_longer(everything(), names_to = "language", values_to = "count") 
dfm

Create plots

Simple

proficiency <- table(rawDF$proficiency)
barplot(proficiency,
   xlab="Proficiency Score", ylab="Score Count", col="skyblue")

Styled

p1 <- ggplot(dfm) +
  aes(x=language, y=count, fill=language) +
  geom_col()
p1

Interactive

ggplotly(p1)
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